語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
到查詢結果
[ subject:"Statistical Software." ]
切換:
標籤
|
MARC模式
|
ISBD
Applied time series analysis and forecasting with Python
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Applied time series analysis and forecasting with Pythonby Changquan Huang, Alla Petukhina.
作者:
Huang, Changquan.
其他作者:
Petukhina, Alla.
出版者:
Cham :Springer International Publishing :2022.
面頁冊數:
x, 372 p. :ill. (chiefly color), digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Time-series analysis.
電子資源:
https://doi.org/10.1007/978-3-031-13584-2
ISBN:
9783031135842$q(electronic bk.)
Applied time series analysis and forecasting with Python
Huang, Changquan.
Applied time series analysis and forecasting with Python
[electronic resource] /by Changquan Huang, Alla Petukhina. - Cham :Springer International Publishing :2022. - x, 372 p. :ill. (chiefly color), digital ;24 cm. - Statistics and computing,2197-1706. - Statistics and computing..
This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
ISBN: 9783031135842$q(electronic bk.)
Standard No.: 10.1007/978-3-031-13584-2doiSubjects--Topical Terms:
181890
Time-series analysis.
LC Class. No.: QA280 / .H83 2022
Dewey Class. No.: 519.55
Applied time series analysis and forecasting with Python
LDR
:02208nmm a2200325 a 4500
001
631531
003
DE-He213
005
20221019035628.0
006
m d
007
cr nn 008maaau
008
230411s2022 sz s 0 eng d
020
$a
9783031135842$q(electronic bk.)
020
$a
9783031135835$q(paper)
024
7
$a
10.1007/978-3-031-13584-2
$2
doi
035
$a
978-3-031-13584-2
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA280
$b
.H83 2022
072
7
$a
PBT
$2
bicssc
072
7
$a
MAT029000
$2
bisacsh
072
7
$a
PBT
$2
thema
082
0 4
$a
519.55
$2
23
090
$a
QA280
$b
.H874 2022
100
1
$a
Huang, Changquan.
$3
937312
245
1 0
$a
Applied time series analysis and forecasting with Python
$h
[electronic resource] /
$c
by Changquan Huang, Alla Petukhina.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
x, 372 p. :
$b
ill. (chiefly color), digital ;
$c
24 cm.
490
1
$a
Statistics and computing,
$x
2197-1706
520
$a
This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
650
0
$a
Time-series analysis.
$3
181890
650
0
$a
Time-series analysis
$x
Forecasting.
$3
937314
650
0
$a
Time-series analysis
$x
Computer programs.
$3
247656
650
0
$a
Python (Computer program language)
$3
215247
650
1 4
$a
Time Series Analysis.
$3
913397
650
2 4
$a
Statistical Software.
$3
925687
650
2 4
$a
Econometrics.
$3
182271
650
2 4
$a
Python.
$3
763308
650
2 4
$a
Machine Learning.
$3
833608
650
2 4
$a
Statistics in Business, Management, Economics, Finance, Insurance.
$3
913173
700
1
$a
Petukhina, Alla.
$3
937313
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer Nature eBook
830
0
$a
Statistics and computing.
$3
558852
856
4 0
$u
https://doi.org/10.1007/978-3-031-13584-2
950
$a
Mathematics and Statistics (SpringerNature-11649)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000221187
電子館藏
1圖書
電子書
EB QA280 .H874 2022 2022
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-3-031-13584-2
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入